کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7378006 | 1480123 | 2016 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A robust nonparametric framework for reconstruction of stochastic differential equation models
ترجمه فارسی عنوان
یک چارچوب غیر پارامتری قوی برای بازسازی مدل های معادلات دیفرانسیل تصادفی
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کلمات کلیدی
معادله دیفرانسیل تصادفی، برآورد پارامتر، روش غیر پارامتری، فرایند ثابت،
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
فیزیک ریاضی
چکیده انگلیسی
In this paper, we employ a nonparametric framework to robustly estimate the functional forms of drift and diffusion terms from discrete stationary time series. The proposed method significantly improves the accuracy of the parameter estimation. In this framework, drift and diffusion coefficients are modeled through orthogonal Legendre polynomials. We employ the least squares regression approach along with the Euler-Maruyama approximation method to learn coefficients of stochastic model. Next, a numerical discrete construction of mean squared prediction error (MSPE) is established to calculate the order of Legendre polynomials in drift and diffusion terms. We show numerically that the new method is robust against the variation in sample size and sampling rate. The performance of our method in comparison with the kernel-based regression (KBR) method is demonstrated through simulation and real data. In case of real dataset, we test our method for discriminating healthy electroencephalogram (EEG) signals from epilepsy ones. We also demonstrate the efficiency of the method through prediction in the financial data. In both simulation and real data, our algorithm outperforms the KBR method.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Physica A: Statistical Mechanics and its Applications - Volume 450, 15 May 2016, Pages 294-304
Journal: Physica A: Statistical Mechanics and its Applications - Volume 450, 15 May 2016, Pages 294-304
نویسندگان
Yalda Rajabzadeh, Amir Hossein Rezaie, Hamidreza Amindavar,